The Keygram of ACIDITY shows four clear segments. Two segents from 0-55s together with 100-150s marking a darker region and 55-100s and 150-180s marking lighter regions. It appears that mainly keys around A minor and A major are consistent through the whole track. Suggesting that the track is mainly in A.

The Chordogram reveals that A minor and A major appear prominently again, just as the Keygram suggested. Again, we see the structures we observed in the keygram between 0-50s and 100-150s.

Between 60-100s, we observe mostly A major, A7, F# minor, D major and D7, aligned with A minor, F major, F7 and D minor. These chords also appear after 150s.

The Chromagram confirms our observations from the Keygram and Chordagram. As we thought, the track mainly appears in A, mainly in the regions between 55s-100s and after 150s. The same segments that we saw in both previous graphs. In the 0-55s and 110-150 segments we also observe occurences of F, C and E.

In this graph, we observe that this track is clearly divided in four segments. The first segment, has a stable intro that builds up. Then, We observe checkerboard patterns in the segments 55-100s and 150-180s, just as we observed before, these regions were mainly in A. Checkerboard patterns suggest that we have homogeneous patterns, therefore the motifs we hear in the track, are in A. Between 100-150s, we see horizontal and vertical lines, meaning it revisits what we heard in the first segment.

This Keygram is more evenly distributed across many keys. The only consistency in this track we observe is in A. It seems that there is no clear structure in this track according to the Keygram, as it seems consistent throughout the whole track.

Confirming our assumptions from the keygram, the chords around A major and A minor are the only consistent chords we observe. As we also saw before, it has a very continuous structure. Besides A, we also observe many different chords present across the whole track.

As we concluded before, there is a consistent bright pattern across A, making it the dominant pitch class. Also, there’s light but consistent use of G#, B, E, and D, adding harmonic color.

Comment:
This self-similarity matrix confirms our assumptions of our track being very continuous, the graph has a very dense grid-like structure, indicating a lot of repetition. This confirms that our track seems very chaotic and all over the place, because there are no parts that occur for a long time.

The track has big spike between 0-10s, the intro, followed by smaller ones till 25s. This suggests a strong intro to grab the listeners attention. Later, at 50s we have another spike of high energy, marking a transition. This aligns with the observations earlier, where we saw an segment from 0-50s and another segment starting at 50s. After this spike, we see a steady progression in energy, with another spike at around 110-120s, marking another transition. The last energy spike we observe is after 150s, confirming our four segment structure.

This track also begins with a large energy spike, followed by many micro-spikes, indicating a lot of variation in the track. Also, the energy spikes seem not that high and also more constant, indicating a lot of variation across the track. As the track progresses, we see larger micro spikes, suggesting that the song builds up in intensity over time. As we observed earlier, this track is rather consistent, does not really have structure and does not stand out in parts, which is clear after analyzing this graph. This consistency keeps the listener engaged, as it creates a driving and hypnotic energy.

At the lower region, the song has high spectral energy in the first 10 seconds, as we observed in the Energy Novelty function before. After this spike, it stagnates till a smaller peak around 60-70s. This Between 90s-150s we see a stable progression with another peak after 150 seconds. This confirms our four segment structure.

This spectral novelty function corresponds with our findings in the Cepstrogram. A local peak at the beginning, followed by another peak at 60 seconds. Continued by a consistent progression till we get another peak at around 160 seconds.

From the edges, we observe bright squares, showing that we have a similar timbre pattern across the track. Around 50-60s we see a notable change, marking a transition. Along 90-150s, we observe a big square, which indicates high similarity during that period. This indicates that we have stable timbre throughout that duration. After that segment, we see another square, indicating a different segment with another consistent timbre pattern.

As we expected from the other graphs, there are no notable changes across the track. This is also seen in this Cepstrogram. The coefficients remain relative uniform, meaning the spectral content has minimal changes. However, at 160-180s we see slightly brigher activity at the lower regions, suggesting a late climax.

This graph confirms our findings from the Cepstrogram. We see a relatively stable progression with high spectral activity towards the end.

The self-similarity matrix is almost uniformly dark with a big squared pattern, suggesting that the timbre is very consistent over time. We see small horizontal and vertical lines, indicating subtle changes, but mostly consistent. Towards the end we see a square that is isolated from the other big square, indicating the last climax at the outro.

In the intro of the track, from 0-40s we observe a very unstable tempo, with alot of fluctuations ranging from 80 BPM to 160 BPM. After this segment, there is quite a stable trend from 50-150s at around 135 BPM. At 150s we see a slight bump from 135 BPM to 130 BPM, marking a transition for the outro. 5-10 seconds later the tempo stabilizes at 135 BPM again.

As expected, GROOVEY maintains a very stable tempo at 145 BPM throughout the whole track. The only disturbance occurs around the end of the track, around 160s we see a short, but very strong dip in BPM. The disruption marks the end of the track.

Using a Random Forest model, we can extract the importance of each feature contributing to our predictions. As observed, approachability, tempo and engaginess contribute the least to our predictions. With this information, we will re-train our model without these features to check if we can yield better results.